Developmental time windows for axon growth influence neuronal network topology

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• 作者：Sol Lim (1) (2)
  Marcus Kaiser (1) (2) (3)
  
  1. Department of Brain and Cognitive Sciences ; Seoul National University ; Seoul ; Republic of Korea
  2. Interdisciplinary Computing and Complex BioSystems Group (ICOS) ; School of Computing Science ; Newcastle University ; Claremont Tower ; Newcastle upon Tyne ; NE1 7RU ; UK
  3. Institute of Neuroscience ; Newcastle University ; Newcastle upon Tyne ; UK
  
• 关键词：Brain connectivity ; Network development ; Computational neuroanatomy ; Complex networks ; Neural networks
• 刊名：Biological Cybernetics
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：109
• 期：2
• 页码：275-286
• 全文大小：1,366 KB
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• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Neurosciences
  Computer Application in Life Sciences
  Neurobiology
  Bioinformatics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-0770

文摘

Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.